विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| अटेंशन मैकेनिज्म (Attention Mechanism)× | BERT फाइन-ट्यूनिंग× | जीपीटी फाइन-ट्यूनिंग× | |
|---|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2015 | 2019 | 2019 |
| प्रवर्तक≠ | Bahdanau, D.; Luong, M.T. | Devlin, J. et al. | Radford, A. et al. (OpenAI) |
| प्रकार≠ | Neural attention layer (encoder-decoder) | Transfer learning (fine-tuning a pre-trained transformer) | Fine-tuning of pretrained autoregressive language models |
| मौलिक स्रोत≠ | Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR. link ↗ | Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL. DOI ↗ | Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Technical Report. link ↗ |
| उपनाम≠ | Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention | BERT İnce Ayar (Fine-Tuning), BERT ince ayar, fine-tuning BERT, transfer learning with BERT | GPT İnce Ayar ve Talimat Uyarlaması, GPT fine-tuning, instruction tuning, LLM fine-tuning |
| संबंधित | 5 | 5 | 5 |
| सारांश≠ | The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector. | BERT fine-tuning, building on the BERT model introduced by Devlin and colleagues in 2019, re-trains a pre-trained BERT model on a small labelled dataset for a target task such as classification, named-entity recognition, or question answering. Through transfer learning it reaches high performance even with relatively little task-specific data. | GPT fine-tuning adapts pretrained autoregressive language models such as GPT-2/3/4 or LLaMA — introduced in OpenAI's 2019 work by Radford and colleagues — to domain-specific data or to instruction following via reinforcement learning from human feedback (RLHF) or DPO. It is used for instruction following, domain adaptation, and generative tasks. |
| ScholarGateडेटासेट ↗ |
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